Equalizer and an equalizer training unit for data-dependent distortion compensation

Abstract

The present disclosure relates to an equalizer training unit for deriving equalization parameters for compensating data-dependent distortion in received samples by use of a training sequence including a sequence p>1 times and cyclically comprising N sub-sequences of respective combinations of L time-domain symbols of a modulation scheme, wherein the N sub-sequences are cyclically arranged in a selected order and such that L−1 symbols of a respective sub-sequence overlap with symbols in the preceding and following sub-sequences. The present disclosure further relates to a training sequence generator unit for generative the training sequence and an equalizer employing the equalizer training unit.

The invention claimed is: 
     
       1. An equalizer training unit for deriving equalization parameters for compensating data-dependent distortion in received samples, the equalizer training unit comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the equalizer training unit to perform:
 obtaining samples corresponding to a training sequence transmitted from a communication device, the training sequence comprising a sequence p>1 times, the sequence cyclically comprising N sub-sequences of respective combinations of L time-domain symbols of a modulation scheme, wherein the N sub-sequences are cyclically arranged in a selected order and such that L−1 symbols of a respective sub-sequence overlap with symbols in preceding and following sub-sequences; 
 estimating N sets of parameters based on the obtained samples and by taking into account periodic properties of the training sequence; 
 deriving an offset based on the estimated N sets of parameters and by taking into account properties of the sequence; and 
 determining the equalization parameters based on the estimated N sets of parameters and the derived offset, 
 wherein p, N, and L are each a respective integer number. 
 
     
     
       2. The equalizer training unit according to  claim 1 , wherein the estimating comprises:
 grouping the obtained samples in p non-overlapping sets of N subsequent samples in accordance with their order of reception; 
 indexing the N subsequent samples of the respective p sets in accordance with their order in the set to obtain N sets of p samples associated with respective indices; and 
 processing the samples associated with the respective indices to obtain the N sets of parameters associated with the respective indices. 
 
     
     
       3. The equalizer training unit according to  claim 2 , wherein the obtained N sets of parameters comprise values indicative of values of the samples associated with the respective indices. 
     
     
       4. The equalizer training unit according to  claim 3 , wherein the deriving comprises correlating the values from the N sets of parameters with the sequence. 
     
     
       5. The equalizer training unit according to  claim 1 , where the determining comprises cyclically shifting the N sets of parameters according to the derived offset and determining the equalization parameters for the corresponding N sub-sequences based on the shifted sets of parameters. 
     
     
       6. The equalizer training unit according to  claim 1 , wherein one or more of the N sub-sequences are repeated two or more times within the sequence, and wherein the determining further comprises combining the corresponding obtained sets of parameters taking into account the occurrences of the respective N sub-sequences within the sequence. 
     
     
       7. The equalizer training unit according to  claim 6 , wherein the respective N sub-sequences are repeated an equal number of times within the sequence. 
     
     
       8. The equalizer training unit according to  claim 1 , wherein the estimating, the deriving, and the determining are performed iteratively, wherein at a respective iteration the estimating, the deriving, and the determining are performed on one or more of the obtained samples. 
     
     
       9. The equalizer training unit according to  claim 1 , wherein the determining further takes into account one or more previously determined equalization parameters. 
     
     
       10. The equalizer training unit according to  claim 9 , wherein the obtained N sets of parameters are averaged by means of an exponential moving averaging with the one or more previously determined N sets of parameters. 
     
     
       11. The equalizer training unit according to  claim 1 , wherein the obtained samples are pre-processed by a linear equalizer and/or a nonlinear equalizer. 
     
     
       12. An equalizer for compensating data-dependent distortion in received samples by equalizing the received samples based on equalization parameters, the equalizer comprising an equalizer training unit configured to derive the equalization parameters, the equalizer training unit comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the equalizer training unit to perform:
 obtaining samples corresponding to a training sequence transmitted from a communication device, the training sequence comprising a sequence p>1 times, the sequence cyclically comprising N sub-sequences of all respective combinations of L time-domain symbols of a modulation scheme, wherein the N sub-sequences are cyclically arranged in a selected order and such that L−1 symbols of a respective sub-sequence overlap with symbols in preceding and following sub-sequences; 
 estimating N sets of parameters based on the obtained samples and by taking into account periodic properties of the training sequence; 
 deriving an offset based on the estimated parameters and by taking into account properties of the sequence; and 
 determining the equalization parameters based on the estimated parameters and the derived offset, 
 wherein p, N, and L are each a respective integer number. 
 
     
     
       13. A training sequence generator unit comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the training sequence generator unit to:
 generate a training sequence for use in a communication device for compensating data-dependent distortion in received data, the training sequence comprising a sequence p>1 times, the sequence cyclically comprising N sub-sequences of all respective combinations of L time-domain symbols of a modulation scheme, wherein the N sets are cyclically arranged in a selected order and such that L−1 symbols of a respective sub-sequence overlap with symbols in preceding and following sub-sequences, and wherein p, N, and L are each a respective integer number.